Marine Sound Separation Algorithm Based on Time-Frequency Interleaved Attention and Integrated Filtering Module

To address the problems of refined perception and discrimination of sound features caused by complex marine soundscapes and the variable characteristics of underwater target signals, this paper proposed a marine sound separation algorithm based on time-frequency interleaved attention and an integrated filtering module(IFM). The algorithm adopted a frequency band division strategy and used an encoder to convert the mixed audio into a time-frequency spectrogram. A multi-scale attention mechanism was utilized to cross-extract time-frequency gains. The IFM efficiently fused the features extracted from the multi-scale convolutional spatial filtering and the self-attention feature dependency pathway with the original features. The fused features were input into a decoder to reconstruct high-quality pure target audio, enhancing the details of the target signals while effectively filtering out background noise and interference. Experimental results on typical marine sound datasets show that the proposed algorithm significantly improves target audio separation performance. In audio separation experiments involving humpback whales mixed with passenger ships and killer whales mixed with passenger ships, the source-to-distortion ratio improvement(SDRi) reaches 8.56 dB and 10.74 dB, respectively, and all performance indicators are superior to those of existing baseline models.